Visualizing the Data.ipynb

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    "# Day and Night Image Classifier\n",
    "---\n",
    "\n",
    "The day/night image dataset consists of 200 RGB color images in two categories: day and night. There are equal numbers of each example: 100 day images and 100 night images.\n",
    "\n",
    "We'd like to build a classifier that can accurately label these images as day or night, and that relies on finding distinguishing features between the two types of images!\n",
    "\n",
    "*Note: All images come from the [AMOS dataset](http://cs.uky.edu/~jacobs/datasets/amos/) (Archive of Many Outdoor Scenes).*\n"
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   "source": [
    "### Import resources\n",
    "\n",
    "Before you get started on the project code, import the libraries and resources that you'll need."
   ]
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  {
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   "source": [
    "import cv2 # computer vision library\n",
    "import helpers\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "%matplotlib inline"
   ]
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   "source": [
    "## Training and Testing Data\n",
    "The 200 day/night images are separated into training and testing datasets. \n",
    "\n",
    "* 60% of these images are training images, for you to use as you create a classifier.\n",
    "* 40% are test images, which will be used to test the accuracy of your classifier.\n",
    "\n",
    "First, we set some variables to keep track of some where our images are stored:\n",
    "\n",
    "    image_dir_training: the directory where our training image data is stored\n",
    "    image_dir_test: the directory where our test image data is stored"
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   "cell_type": "code",
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   "source": [
    "# Image data directories\n",
    "image_dir_training = \"day_night_images/training/\"\n",
    "image_dir_test = \"day_night_images/test/\""
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Load the datasets\n",
    "\n",
    "These first few lines of code will load the training day/night images and store all of them in a variable, `IMAGE_LIST`. This list contains the images and their associated label (\"day\" or \"night\"). \n",
    "\n",
    "For example, the first image-label pair in `IMAGE_LIST` can be accessed by index: \n",
    "``` IMAGE_LIST[0][:]```.\n"
   ]
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   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# Using the load_dataset function in helpers.py\n",
    "# Load training data\n",
    "IMAGE_LIST = helpers.load_dataset(image_dir_training)\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 1. Visualize the input images\n"
   ]
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      "at async w.clearResult (/Users/sami/.vscode/extensions/ms-python.python-2020.5.86806/out/client/extension.js:16:567473)",
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   "source": [
    "# TO DO: ADD IMAGE FILES AND FIX NIGHT CLASSIFICATION BELOW\n",
    "# Select an image and its label by list index\n",
    "image_index = 0\n",
    "selected_image = IMAGE_LIST[image_index][0]\n",
    "selected_label = IMAGE_LIST[image_index][1]\n",
    "\n",
    "## TODO: Print out 1. The shape of the image and 2. The image's label `selected_label`\n",
    "print('Image dimensions:', selected_image.shape)\n",
    "print('Image label:', selected_label)\n",
    "\n",
    "## TODO: Display a night image\n",
    "# Note the differences between the day and night images\n",
    "# Any measurable differences can be used to classify these images\n",
    "image_index = 5\n",
    "selected_image = IMAGE_LIST[image_index][0]\n",
    "selected_label = IMAGE_LIST[image_index][1]\n",
    "print('Image dimensions:', selected_image.shape)\n",
    "print('Image label:', selected_label)\n",
    "\n",
    "plt.imshow(selected_image)"
   ]
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